Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery 1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin-staining of processed tissue is time-, resource-, and labor-intensive 2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed pathology workforce 4. Here, we report a parallel workflow that combines stimulated Raman histology (SRH) 5-7 , a label-free optical imaging method, and deep convolutional neural networks (CNN) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNN, trained on over 2.5 million SRH images, predicts brain tumor diagnosis in the operating room in under 150 seconds, an order of magnitude faster than conventional techniques (e.g., 20-30 minutes) 2. In a multicenter, prospective clinical trial (n = 278) we demonstrated that CNN-based diagnosis of SRH images was non-inferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% vs. 93.9%). Our CNN learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. Additionally, we implemented a semantic segmentation method to identify tumor infiltrated, diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complimentary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.
Background Detection of glioma recurrence remains a challenge in modern neuro-oncology. Noninvasive radiographic imaging is unable to definitively differentiate true recurrence versus pseudoprogression. Even in biopsied tissue, it can be challenging to differentiate recurrent tumor and treatment effect. We hypothesized that intraoperative stimulated Raman histology (SRH) and deep neural networks can be used to improve the intraoperative detection of glioma recurrence. Methods We used fiber-laser-based SRH, a label-free, non-consumptive, high-resolution microscopy method (<60 secs per 1 x 1 mm2) to image a cohort of patients (n = 35) with suspected recurrent gliomas who underwent biopsy or resection. The SRH images were then used to train a convolutional neural network (CNN) and develop an inference algorithm to detect viable recurrent glioma. Following network training, the performance of the CNN was tested for diagnostic accuracy in a retrospective cohort (n = 48). Results Using patch-level CNN predictions, the inference algorithm returned a single Bernoulli distribution for the probability of tumor recurrence for each surgical specimen or patient. The external SRH validation dataset consisted of 48 patients (recurrent, 30; pseudoprogression, 18), and we achieved a diagnostic accuracy of 95.8%. Conclusion SRH with CNN-based diagnosis can be used to improve the intraoperative detection of glioma recurrence in near-real time. Our results provide insight into how optical imaging and computer vision can be combined to augment conventional diagnostic methods and improve the quality of specimen sampling at glioma recurrence.
BACKGROUND:Accurate specimen analysis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative specimen interpretation can be challenging because of the wide range of skull base pathologies and lack of intraoperative pathology resources.OBJECTIVE:To develop an independent and parallel intraoperative workflow that can provide rapid and accurate skull base tumor specimen analysis using label-free optical imaging and artificial intelligence.METHODS:We used a fiber laser–based, label-free, nonconsumptive, high-resolution microscopy method (<60 seconds per 1 × 1 mm2), called stimulated Raman histology (SRH), to image a consecutive, multicenter cohort of patients with skull base tumor. SRH images were then used to train a convolutional neural network model using 3 representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive learning. Our trained convolutional neural network models were tested on a held-out, multicenter SRH data set.RESULTS:SRH was able to image the diagnostic features of both benign and malignant skull base tumors. Of the 3 representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to segment tumor-normal margins and detect regions of microscopic tumor infiltration in meningioma SRH images.CONCLUSION:SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making.
Accurate intraoperative diagnosis of recurrence versus treatment effect (TE) is essential for determining the management of suspected recurrent gliomas. Cytologic and histoarchitectural changes related to chemoradiation overlap with common findings in recurrent tumors (e.g. atypia, abnormal vasculature, necrosis). Moreover, H&E tissue processing artifact complicates interpretation. Stimulated Raman histology (SRH) uses the intrinsic biochemical properties of fresh, unprocessed surgical specimens to provide rapid label-free digital histologic images. Here, we report an automated technique using deep convolutional neural networks (ConvNet) that differentiates recurrent glioma and TE in fresh surgical specimens imaged using SRH with equivalent accuracy and 10x faster (tissue-to-diagnosis, 2 minutes) than conventional methods. Our ConvNet, based on Google’s Inception-ResNet-v2 architecture, was first trained on 3.6 million SRH images from 441 patients with the most common brain tumor subtypes. To optimize the network for classifying glioma recurrence, we used cross-validation (CV) on 35 patients (24 recurrent, 9 TE) for model hyperparameter tuning and to identify an optimal probability threshold to classify recurrence. To perform rigorous model validation, we used a 50 patient external testing set to evaluate overall model accuracy. Over 5 iterations of CV, the mean held-out classification accuracy was 94.8% (range, 91.4 - 97.1%). Using ROC analysis, we found that a probability of recurrence greater than 25% was the optimal threshold to render a recurrence diagnosis for whole-slide SRH images. Using our external testing set, we achieved a classification accuracy of 96% (total 48/50; 30/30 recurrences, 18/20 TE). Moreover, our method effectively identifies regions of glioma recurrence in whole slide SRH at no additional computational cost. Our study demonstrates the feasibility of applying deep learning for intraoperative diagnosis of recurrent gliomas in SRH imaged tissues. In the future, ConvNets may ultimately be used to guide decision-making in the surgical care of recurrent gliomas, independent of conventional neuropathology resources.
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